For causal generation, [1]} rashkin2018event2mind aimed to generate the likely intents and reactions of the event's participants, given a short free-form textual event. [2]} sap2019atomic trained a multi-task model for fine-grained kinds of If-Then commonsense reasoning. However, the causal semantics considered in their work are restricted to a narrow space, and their models are trained on no more than one million examples. Further, their resource was based-on crowdsourcing, which carries risks of human bias [3]}, [4]}. We harvest a significantly larger, open coverage causal corpus,While we avoid pitfalls of elicitation, we acknowledge that like any corpus-extracted resource ours may suffer from reporting bias [5]}: some types of causes or effects that are known to humans but rarely or ever explicitly stated. related in approach to DisSent [6]} but larger, focused on causality, and aimed primarily at generation rather than sentence representation learning.
